Overview

Dataset statistics

Number of variables23
Number of observations1000
Missing cells317
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory179.8 KiB
Average record size in memory184.1 B

Variable types

Text4
Categorical5
Numeric11
DateTime1
Boolean2

Alerts

deceased_indicator has constant value ""Constant
country has constant value ""Constant
postcode is highly overall correlated with property_valuation and 1 other fieldsHigh correlation
property_valuation is highly overall correlated with postcodeHigh correlation
Unnamed: 16 is highly overall correlated with Unnamed: 17 and 2 other fieldsHigh correlation
Unnamed: 17 is highly overall correlated with Unnamed: 16 and 2 other fieldsHigh correlation
Unnamed: 18 is highly overall correlated with Unnamed: 16 and 2 other fieldsHigh correlation
Unnamed: 19 is highly overall correlated with Unnamed: 16 and 2 other fieldsHigh correlation
Unnamed: 20 is highly overall correlated with Rank and 1 other fieldsHigh correlation
Rank is highly overall correlated with Unnamed: 20 and 1 other fieldsHigh correlation
Value is highly overall correlated with Unnamed: 20 and 1 other fieldsHigh correlation
state is highly overall correlated with postcodeHigh correlation
last_name has 29 (2.9%) missing valuesMissing
DOB has 17 (1.7%) missing valuesMissing
job_title has 106 (10.6%) missing valuesMissing
job_industry_category has 165 (16.5%) missing valuesMissing
address has unique valuesUnique

Reproduction

Analysis started2023-08-03 14:17:12.179487
Analysis finished2023-08-03 14:17:21.242016
Duration9.06 seconds
Software versionydata-profiling vv4.4.0
Download configurationconfig.json

Variables

Distinct940
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-08-03T19:47:21.347449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length13
Median length11
Mean length6.087
Min length2

Characters and Unicode

Total characters6087
Distinct characters52
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique883 ?
Unique (%)88.3%

Sample

1st rowChickie
2nd rowMorly
3rd rowArdelis
4th rowLucine
5th rowMelinda
ValueCountFrequency (%)
rozamond 3
 
0.3%
mandie 3
 
0.3%
dorian 3
 
0.3%
inglebert 2
 
0.2%
kizzee 2
 
0.2%
latrena 2
 
0.2%
geoff 2
 
0.2%
wyn 2
 
0.2%
art 2
 
0.2%
cami 2
 
0.2%
Other values (930) 977
97.7%
2023-08-03T19:47:21.556417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 702
 
11.5%
a 631
 
10.4%
i 566
 
9.3%
n 488
 
8.0%
r 434
 
7.1%
l 414
 
6.8%
o 303
 
5.0%
t 223
 
3.7%
d 193
 
3.2%
s 168
 
2.8%
Other values (42) 1965
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5086
83.6%
Uppercase Letter 1000
 
16.4%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 702
13.8%
a 631
12.4%
i 566
11.1%
n 488
9.6%
r 434
8.5%
l 414
8.1%
o 303
 
6.0%
t 223
 
4.4%
d 193
 
3.8%
s 168
 
3.3%
Other values (16) 964
19.0%
Uppercase Letter
ValueCountFrequency (%)
A 88
 
8.8%
C 80
 
8.0%
M 75
 
7.5%
D 71
 
7.1%
S 64
 
6.4%
R 63
 
6.3%
L 61
 
6.1%
B 56
 
5.6%
G 54
 
5.4%
K 54
 
5.4%
Other values (15) 334
33.4%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6086
> 99.9%
Common 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 702
 
11.5%
a 631
 
10.4%
i 566
 
9.3%
n 488
 
8.0%
r 434
 
7.1%
l 414
 
6.8%
o 303
 
5.0%
t 223
 
3.7%
d 193
 
3.2%
s 168
 
2.8%
Other values (41) 1964
32.3%
Common
ValueCountFrequency (%)
- 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6087
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 702
 
11.5%
a 631
 
10.4%
i 566
 
9.3%
n 488
 
8.0%
r 434
 
7.1%
l 414
 
6.8%
o 303
 
5.0%
t 223
 
3.7%
d 193
 
3.2%
s 168
 
2.8%
Other values (42) 1965
32.3%

last_name
Text

MISSING 

Distinct961
Distinct (%)99.0%
Missing29
Missing (%)2.9%
Memory size7.9 KiB
2023-08-03T19:47:21.693407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length21
Median length12
Mean length7.0267765
Min length3

Characters and Unicode

Total characters6823
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique951 ?
Unique (%)97.9%

Sample

1st rowBrister
2nd rowGenery
3rd rowForrester
4th rowStutt
5th rowHadlee
ValueCountFrequency (%)
de 3
 
0.3%
van 3
 
0.3%
den 3
 
0.3%
crellim 2
 
0.2%
sissel 2
 
0.2%
hallt 2
 
0.2%
minshall 2
 
0.2%
burgoine 2
 
0.2%
eade 2
 
0.2%
velde 2
 
0.2%
Other values (960) 963
97.7%
2023-08-03T19:47:21.901380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 707
 
10.4%
a 529
 
7.8%
n 500
 
7.3%
r 454
 
6.7%
o 441
 
6.5%
l 410
 
6.0%
i 406
 
6.0%
t 361
 
5.3%
s 316
 
4.6%
d 207
 
3.0%
Other values (44) 2492
36.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5783
84.8%
Uppercase Letter 1014
 
14.9%
Space Separator 15
 
0.2%
Other Punctuation 10
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 707
12.2%
a 529
 
9.1%
n 500
 
8.6%
r 454
 
7.9%
o 441
 
7.6%
l 410
 
7.1%
i 406
 
7.0%
t 361
 
6.2%
s 316
 
5.5%
d 207
 
3.6%
Other values (16) 1452
25.1%
Uppercase Letter
ValueCountFrequency (%)
B 120
 
11.8%
S 94
 
9.3%
C 93
 
9.2%
M 79
 
7.8%
D 67
 
6.6%
P 58
 
5.7%
H 56
 
5.5%
A 51
 
5.0%
G 48
 
4.7%
R 43
 
4.2%
Other values (15) 305
30.1%
Space Separator
ValueCountFrequency (%)
15
100.0%
Other Punctuation
ValueCountFrequency (%)
' 10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6797
99.6%
Common 26
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 707
 
10.4%
a 529
 
7.8%
n 500
 
7.4%
r 454
 
6.7%
o 441
 
6.5%
l 410
 
6.0%
i 406
 
6.0%
t 361
 
5.3%
s 316
 
4.6%
d 207
 
3.0%
Other values (41) 2466
36.3%
Common
ValueCountFrequency (%)
15
57.7%
' 10
38.5%
- 1
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 707
 
10.4%
a 529
 
7.8%
n 500
 
7.3%
r 454
 
6.7%
o 441
 
6.5%
l 410
 
6.0%
i 406
 
6.0%
t 361
 
5.3%
s 316
 
4.6%
d 207
 
3.0%
Other values (44) 2492
36.5%

gender
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Female
513 
Male
470 
U
 
17

Length

Max length6
Median length6
Mean length4.975
Min length1

Characters and Unicode

Total characters4975
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 513
51.3%
Male 470
47.0%
U 17
 
1.7%

Length

2023-08-03T19:47:21.980821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-03T19:47:22.043926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
female 513
51.3%
male 470
47.0%
u 17
 
1.7%

Most occurring characters

ValueCountFrequency (%)
e 1496
30.1%
a 983
19.8%
l 983
19.8%
F 513
 
10.3%
m 513
 
10.3%
M 470
 
9.4%
U 17
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3975
79.9%
Uppercase Letter 1000
 
20.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1496
37.6%
a 983
24.7%
l 983
24.7%
m 513
 
12.9%
Uppercase Letter
ValueCountFrequency (%)
F 513
51.3%
M 470
47.0%
U 17
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 4975
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1496
30.1%
a 983
19.8%
l 983
19.8%
F 513
 
10.3%
m 513
 
10.3%
M 470
 
9.4%
U 17
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4975
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1496
30.1%
a 983
19.8%
l 983
19.8%
F 513
 
10.3%
m 513
 
10.3%
M 470
 
9.4%
U 17
 
0.3%
Distinct100
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.836
Minimum0
Maximum99
Zeros9
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-03T19:47:22.106855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q126.75
median51
Q372
95-th percentile94
Maximum99
Range99
Interquartile range (IQR)45.25

Descriptive statistics

Standard deviation27.796686
Coefficient of variation (CV)0.55776319
Kurtosis-1.0880489
Mean49.836
Median Absolute Deviation (MAD)22.5
Skewness-0.065621862
Sum49836
Variance772.65576
MonotonicityNot monotonic
2023-08-03T19:47:22.177271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 20
 
2.0%
59 18
 
1.8%
42 17
 
1.7%
70 17
 
1.7%
11 16
 
1.6%
37 16
 
1.6%
47 15
 
1.5%
62 14
 
1.4%
84 14
 
1.4%
79 14
 
1.4%
Other values (90) 839
83.9%
ValueCountFrequency (%)
0 9
0.9%
1 8
0.8%
2 9
0.9%
3 9
0.9%
4 10
1.0%
5 13
1.3%
6 10
1.0%
7 13
1.3%
8 7
0.7%
9 5
 
0.5%
ValueCountFrequency (%)
99 9
0.9%
98 6
0.6%
97 11
1.1%
96 9
0.9%
95 8
0.8%
94 12
1.2%
93 9
0.9%
92 5
0.5%
91 8
0.8%
90 6
0.6%

DOB
Date

MISSING 

Distinct958
Distinct (%)97.5%
Missing17
Missing (%)1.7%
Memory size7.9 KiB
Minimum1938-06-08 00:00:00
Maximum2002-02-27 00:00:00
2023-08-03T19:47:22.258895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:22.334205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

job_title
Text

MISSING 

Distinct184
Distinct (%)20.6%
Missing106
Missing (%)10.6%
Memory size7.9 KiB
2023-08-03T19:47:22.463622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length36
Median length25
Mean length18.088367
Min length5

Characters and Unicode

Total characters16171
Distinct characters47
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)5.0%

Sample

1st rowGeneral Manager
2nd rowStructural Engineer
3rd rowSenior Cost Accountant
4th rowAccount Representative III
5th rowFinancial Analyst
ValueCountFrequency (%)
engineer 131
 
6.3%
assistant 82
 
3.9%
manager 76
 
3.7%
analyst 66
 
3.2%
iv 52
 
2.5%
iii 50
 
2.4%
vp 46
 
2.2%
ii 44
 
2.1%
senior 44
 
2.1%
sales 44
 
2.1%
Other values (117) 1444
69.5%
2023-08-03T19:47:22.675290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1578
 
9.8%
n 1279
 
7.9%
a 1253
 
7.7%
t 1218
 
7.5%
1185
 
7.3%
i 1124
 
7.0%
r 1083
 
6.7%
s 966
 
6.0%
o 773
 
4.8%
c 678
 
4.2%
Other values (37) 5034
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12650
78.2%
Uppercase Letter 2328
 
14.4%
Space Separator 1185
 
7.3%
Other Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1578
12.5%
n 1279
10.1%
a 1253
9.9%
t 1218
9.6%
i 1124
8.9%
r 1083
8.6%
s 966
7.6%
o 773
 
6.1%
c 678
 
5.4%
l 532
 
4.2%
Other values (14) 2166
17.1%
Uppercase Letter
ValueCountFrequency (%)
I 351
15.1%
A 346
14.9%
S 271
11.6%
E 201
8.6%
P 197
8.5%
C 156
6.7%
M 134
 
5.8%
D 121
 
5.2%
V 98
 
4.2%
T 78
 
3.4%
Other values (11) 375
16.1%
Space Separator
ValueCountFrequency (%)
1185
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14978
92.6%
Common 1193
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1578
 
10.5%
n 1279
 
8.5%
a 1253
 
8.4%
t 1218
 
8.1%
i 1124
 
7.5%
r 1083
 
7.2%
s 966
 
6.4%
o 773
 
5.2%
c 678
 
4.5%
l 532
 
3.6%
Other values (35) 4494
30.0%
Common
ValueCountFrequency (%)
1185
99.3%
/ 8
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16171
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1578
 
9.8%
n 1279
 
7.9%
a 1253
 
7.7%
t 1218
 
7.5%
1185
 
7.3%
i 1124
 
7.0%
r 1083
 
6.7%
s 966
 
6.0%
o 773
 
4.8%
c 678
 
4.2%
Other values (37) 5034
31.1%

job_industry_category
Categorical

MISSING 

Distinct9
Distinct (%)1.1%
Missing165
Missing (%)16.5%
Memory size7.9 KiB
Financial Services
203 
Manufacturing
199 
Health
152 
Retail
78 
Property
64 
Other values (4)
139 

Length

Max length18
Median length13
Mean length11.31976
Min length2

Characters and Unicode

Total characters9452
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManufacturing
2nd rowProperty
3rd rowFinancial Services
4th rowManufacturing
5th rowFinancial Services

Common Values

ValueCountFrequency (%)
Financial Services 203
20.3%
Manufacturing 199
19.9%
Health 152
15.2%
Retail 78
 
7.8%
Property 64
 
6.4%
IT 51
 
5.1%
Entertainment 37
 
3.7%
Argiculture 26
 
2.6%
Telecommunications 25
 
2.5%
(Missing) 165
16.5%

Length

2023-08-03T19:47:22.760844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-03T19:47:22.842622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
financial 203
19.6%
services 203
19.6%
manufacturing 199
19.2%
health 152
14.6%
retail 78
 
7.5%
property 64
 
6.2%
it 51
 
4.9%
entertainment 37
 
3.6%
argiculture 26
 
2.5%
telecommunications 25
 
2.4%

Most occurring characters

ValueCountFrequency (%)
a 1096
11.6%
i 999
10.6%
n 965
10.2%
e 850
 
9.0%
c 681
 
7.2%
t 655
 
6.9%
r 619
 
6.5%
l 484
 
5.1%
u 475
 
5.0%
s 228
 
2.4%
Other values (19) 2400
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8160
86.3%
Uppercase Letter 1089
 
11.5%
Space Separator 203
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1096
13.4%
i 999
12.2%
n 965
11.8%
e 850
10.4%
c 681
8.3%
t 655
8.0%
r 619
7.6%
l 484
5.9%
u 475
5.8%
s 228
 
2.8%
Other values (8) 1108
13.6%
Uppercase Letter
ValueCountFrequency (%)
F 203
18.6%
S 203
18.6%
M 199
18.3%
H 152
14.0%
R 78
 
7.2%
T 76
 
7.0%
P 64
 
5.9%
I 51
 
4.7%
E 37
 
3.4%
A 26
 
2.4%
Space Separator
ValueCountFrequency (%)
203
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9249
97.9%
Common 203
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1096
11.8%
i 999
10.8%
n 965
10.4%
e 850
 
9.2%
c 681
 
7.4%
t 655
 
7.1%
r 619
 
6.7%
l 484
 
5.2%
u 475
 
5.1%
s 228
 
2.5%
Other values (18) 2197
23.8%
Common
ValueCountFrequency (%)
203
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1096
11.6%
i 999
10.6%
n 965
10.2%
e 850
 
9.0%
c 681
 
7.2%
t 655
 
6.9%
r 619
 
6.5%
l 484
 
5.1%
u 475
 
5.0%
s 228
 
2.4%
Other values (19) 2400
25.4%

wealth_segment
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Mass Customer
508 
High Net Worth
251 
Affluent Customer
241 

Length

Max length17
Median length13
Mean length14.215
Min length13

Characters and Unicode

Total characters14215
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMass Customer
2nd rowMass Customer
3rd rowAffluent Customer
4th rowAffluent Customer
5th rowAffluent Customer

Common Values

ValueCountFrequency (%)
Mass Customer 508
50.8%
High Net Worth 251
25.1%
Affluent Customer 241
24.1%

Length

2023-08-03T19:47:22.924863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-03T19:47:22.991957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
customer 749
33.3%
mass 508
22.6%
high 251
 
11.2%
net 251
 
11.2%
worth 251
 
11.2%
affluent 241
 
10.7%

Most occurring characters

ValueCountFrequency (%)
s 1765
12.4%
t 1492
10.5%
1251
 
8.8%
e 1241
 
8.7%
r 1000
 
7.0%
o 1000
 
7.0%
u 990
 
7.0%
C 749
 
5.3%
m 749
 
5.3%
a 508
 
3.6%
Other values (11) 3470
24.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10713
75.4%
Uppercase Letter 2251
 
15.8%
Space Separator 1251
 
8.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 1765
16.5%
t 1492
13.9%
e 1241
11.6%
r 1000
9.3%
o 1000
9.3%
u 990
9.2%
m 749
7.0%
a 508
 
4.7%
h 502
 
4.7%
f 482
 
4.5%
Other values (4) 984
9.2%
Uppercase Letter
ValueCountFrequency (%)
C 749
33.3%
M 508
22.6%
H 251
 
11.2%
N 251
 
11.2%
W 251
 
11.2%
A 241
 
10.7%
Space Separator
ValueCountFrequency (%)
1251
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12964
91.2%
Common 1251
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 1765
13.6%
t 1492
11.5%
e 1241
9.6%
r 1000
 
7.7%
o 1000
 
7.7%
u 990
 
7.6%
C 749
 
5.8%
m 749
 
5.8%
a 508
 
3.9%
M 508
 
3.9%
Other values (10) 2962
22.8%
Common
ValueCountFrequency (%)
1251
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14215
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 1765
12.4%
t 1492
10.5%
1251
 
8.8%
e 1241
 
8.7%
r 1000
 
7.0%
o 1000
 
7.0%
u 990
 
7.0%
C 749
 
5.3%
m 749
 
5.3%
a 508
 
3.6%
Other values (11) 3470
24.4%

deceased_indicator
Boolean

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
1000 
ValueCountFrequency (%)
False 1000
100.0%
2023-08-03T19:47:23.048936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

owns_car
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
507 
True
493 
ValueCountFrequency (%)
False 507
50.7%
True 493
49.3%
2023-08-03T19:47:23.095969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

tenure
Real number (ℝ)

Distinct23
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.388
Minimum0
Maximum22
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-03T19:47:23.144413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median11
Q315
95-th percentile20
Maximum22
Range22
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.0371449
Coefficient of variation (CV)0.44232042
Kurtosis-0.81281522
Mean11.388
Median Absolute Deviation (MAD)4
Skewness0.070890798
Sum11388
Variance25.372829
MonotonicityNot monotonic
2023-08-03T19:47:23.200440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
9 79
 
7.9%
13 74
 
7.4%
11 68
 
6.8%
10 63
 
6.3%
12 61
 
6.1%
5 60
 
6.0%
7 60
 
6.0%
17 59
 
5.9%
15 58
 
5.8%
8 55
 
5.5%
Other values (13) 363
36.3%
ValueCountFrequency (%)
0 2
 
0.2%
1 8
 
0.8%
2 15
 
1.5%
3 26
 
2.6%
4 36
3.6%
5 60
6.0%
6 45
4.5%
7 60
6.0%
8 55
5.5%
9 79
7.9%
ValueCountFrequency (%)
22 12
 
1.2%
21 24
 
2.4%
20 22
 
2.2%
19 34
3.4%
18 36
3.6%
17 59
5.9%
16 49
4.9%
15 58
5.8%
14 54
5.4%
13 74
7.4%

address
Text

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-08-03T19:47:23.322788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length26
Median length23
Mean length17.582
Min length9

Characters and Unicode

Total characters17582
Distinct characters60
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st row45 Shopko Center
2nd row14 Mccormick Park
3rd row5 Colorado Crossing
4th row207 Annamark Plaza
5th row115 Montana Place
ValueCountFrequency (%)
park 59
 
1.9%
crossing 59
 
1.9%
center 58
 
1.9%
avenue 55
 
1.8%
street 55
 
1.8%
lane 55
 
1.8%
point 54
 
1.7%
hill 51
 
1.6%
plaza 50
 
1.6%
court 49
 
1.6%
Other values (1137) 2563
82.5%
2023-08-03T19:47:23.545854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2108
 
12.0%
e 1394
 
7.9%
a 1152
 
6.6%
r 1072
 
6.1%
n 884
 
5.0%
o 787
 
4.5%
i 748
 
4.3%
l 732
 
4.2%
t 645
 
3.7%
s 469
 
2.7%
Other values (50) 7591
43.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10401
59.2%
Decimal Number 2975
 
16.9%
Space Separator 2108
 
12.0%
Uppercase Letter 2098
 
11.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1394
13.4%
a 1152
11.1%
r 1072
10.3%
n 884
8.5%
o 787
 
7.6%
i 748
 
7.2%
l 732
 
7.0%
t 645
 
6.2%
s 469
 
4.5%
d 318
 
3.1%
Other values (16) 2200
21.2%
Uppercase Letter
ValueCountFrequency (%)
P 337
16.1%
C 272
13.0%
S 167
 
8.0%
M 142
 
6.8%
A 142
 
6.8%
T 114
 
5.4%
R 113
 
5.4%
D 112
 
5.3%
L 108
 
5.1%
H 99
 
4.7%
Other values (13) 492
23.5%
Decimal Number
ValueCountFrequency (%)
6 330
11.1%
7 317
10.7%
0 315
10.6%
2 305
10.3%
3 300
10.1%
1 294
9.9%
5 293
9.8%
9 279
9.4%
8 273
9.2%
4 269
9.0%
Space Separator
ValueCountFrequency (%)
2108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12499
71.1%
Common 5083
28.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1394
 
11.2%
a 1152
 
9.2%
r 1072
 
8.6%
n 884
 
7.1%
o 787
 
6.3%
i 748
 
6.0%
l 732
 
5.9%
t 645
 
5.2%
s 469
 
3.8%
P 337
 
2.7%
Other values (39) 4279
34.2%
Common
ValueCountFrequency (%)
2108
41.5%
6 330
 
6.5%
7 317
 
6.2%
0 315
 
6.2%
2 305
 
6.0%
3 300
 
5.9%
1 294
 
5.8%
5 293
 
5.8%
9 279
 
5.5%
8 273
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2108
 
12.0%
e 1394
 
7.9%
a 1152
 
6.6%
r 1072
 
6.1%
n 884
 
5.0%
o 787
 
4.5%
i 748
 
4.3%
l 732
 
4.2%
t 645
 
3.7%
s 469
 
2.7%
Other values (50) 7591
43.2%

postcode
Real number (ℝ)

HIGH CORRELATION 

Distinct522
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3019.227
Minimum2000
Maximum4879
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-03T19:47:23.632485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2046
Q12209
median2800
Q33845.5
95-th percentile4508.05
Maximum4879
Range2879
Interquartile range (IQR)1636.5

Descriptive statistics

Standard deviation848.89577
Coefficient of variation (CV)0.28116328
Kurtosis-1.1424982
Mean3019.227
Median Absolute Deviation (MAD)635.5
Skewness0.49210793
Sum3019227
Variance720624.02
MonotonicityNot monotonic
2023-08-03T19:47:23.699822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2145 9
 
0.9%
2232 9
 
0.9%
2750 7
 
0.7%
3977 7
 
0.7%
2148 7
 
0.7%
3029 7
 
0.7%
4207 7
 
0.7%
2168 7
 
0.7%
3163 6
 
0.6%
2121 6
 
0.6%
Other values (512) 928
92.8%
ValueCountFrequency (%)
2000 1
 
0.1%
2007 3
0.3%
2009 2
0.2%
2010 4
0.4%
2011 4
0.4%
2015 1
 
0.1%
2016 2
0.2%
2017 1
 
0.1%
2019 3
0.3%
2022 1
 
0.1%
ValueCountFrequency (%)
4879 1
 
0.1%
4852 1
 
0.1%
4818 2
0.2%
4817 2
0.2%
4814 3
0.3%
4744 1
 
0.1%
4740 2
0.2%
4720 1
 
0.1%
4717 1
 
0.1%
4710 1
 
0.1%

state
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
NSW
506 
VIC
266 
QLD
228 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3000
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQLD
2nd rowNSW
3rd rowVIC
4th rowQLD
5th rowNSW

Common Values

ValueCountFrequency (%)
NSW 506
50.6%
VIC 266
26.6%
QLD 228
22.8%

Length

2023-08-03T19:47:23.767154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-03T19:47:23.828832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nsw 506
50.6%
vic 266
26.6%
qld 228
22.8%

Most occurring characters

ValueCountFrequency (%)
N 506
16.9%
S 506
16.9%
W 506
16.9%
V 266
8.9%
I 266
8.9%
C 266
8.9%
Q 228
7.6%
L 228
7.6%
D 228
7.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 506
16.9%
S 506
16.9%
W 506
16.9%
V 266
8.9%
I 266
8.9%
C 266
8.9%
Q 228
7.6%
L 228
7.6%
D 228
7.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 3000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 506
16.9%
S 506
16.9%
W 506
16.9%
V 266
8.9%
I 266
8.9%
C 266
8.9%
Q 228
7.6%
L 228
7.6%
D 228
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 506
16.9%
S 506
16.9%
W 506
16.9%
V 266
8.9%
I 266
8.9%
C 266
8.9%
Q 228
7.6%
L 228
7.6%
D 228
7.6%

country
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Australia
1000 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters9000
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAustralia
2nd rowAustralia
3rd rowAustralia
4th rowAustralia
5th rowAustralia

Common Values

ValueCountFrequency (%)
Australia 1000
100.0%

Length

2023-08-03T19:47:23.967601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-03T19:47:24.019916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
australia 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
a 2000
22.2%
A 1000
11.1%
u 1000
11.1%
s 1000
11.1%
t 1000
11.1%
r 1000
11.1%
l 1000
11.1%
i 1000
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8000
88.9%
Uppercase Letter 1000
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2000
25.0%
u 1000
12.5%
s 1000
12.5%
t 1000
12.5%
r 1000
12.5%
l 1000
12.5%
i 1000
12.5%
Uppercase Letter
ValueCountFrequency (%)
A 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2000
22.2%
A 1000
11.1%
u 1000
11.1%
s 1000
11.1%
t 1000
11.1%
r 1000
11.1%
l 1000
11.1%
i 1000
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2000
22.2%
A 1000
11.1%
u 1000
11.1%
s 1000
11.1%
t 1000
11.1%
r 1000
11.1%
l 1000
11.1%
i 1000
11.1%

property_valuation
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.397
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-03T19:47:24.063500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median8
Q39
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7588045
Coefficient of variation (CV)0.37296261
Kurtosis-0.37127999
Mean7.397
Median Absolute Deviation (MAD)2
Skewness-0.55761121
Sum7397
Variance7.611002
MonotonicityNot monotonic
2023-08-03T19:47:24.118954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9 173
17.3%
8 162
16.2%
7 138
13.8%
10 116
11.6%
6 70
7.0%
11 62
 
6.2%
5 57
 
5.7%
4 53
 
5.3%
3 51
 
5.1%
12 46
 
4.6%
Other values (2) 72
7.2%
ValueCountFrequency (%)
1 30
 
3.0%
2 42
 
4.2%
3 51
 
5.1%
4 53
 
5.3%
5 57
 
5.7%
6 70
7.0%
7 138
13.8%
8 162
16.2%
9 173
17.3%
10 116
11.6%
ValueCountFrequency (%)
12 46
 
4.6%
11 62
 
6.2%
10 116
11.6%
9 173
17.3%
8 162
16.2%
7 138
13.8%
6 70
7.0%
5 57
 
5.7%
4 53
 
5.3%
3 51
 
5.1%

Unnamed: 16
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74384
Minimum0.4
Maximum1.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-03T19:47:24.186849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.42
Q10.55
median0.75
Q30.93
95-th percentile1.07
Maximum1.1
Range0.7
Interquartile range (IQR)0.38

Descriptive statistics

Standard deviation0.21214821
Coefficient of variation (CV)0.28520677
Kurtosis-1.2603279
Mean0.74384
Median Absolute Deviation (MAD)0.19
Skewness0.018602114
Sum743.84
Variance0.045006861
MonotonicityNot monotonic
2023-08-03T19:47:24.258706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 25
 
2.5%
0.55 25
 
2.5%
1.08 25
 
2.5%
0.41 25
 
2.5%
0.43 24
 
2.4%
0.46 22
 
2.2%
0.61 19
 
1.9%
1.07 19
 
1.9%
0.93 19
 
1.9%
0.63 18
 
1.8%
Other values (61) 779
77.9%
ValueCountFrequency (%)
0.4 15
1.5%
0.41 25
2.5%
0.42 17
1.7%
0.43 24
2.4%
0.44 7
 
0.7%
0.45 12
1.2%
0.46 22
2.2%
0.47 12
1.2%
0.48 17
1.7%
0.49 10
 
1.0%
ValueCountFrequency (%)
1.1 12
1.2%
1.09 9
 
0.9%
1.08 25
2.5%
1.07 19
1.9%
1.06 15
1.5%
1.05 15
1.5%
1.04 17
1.7%
1.03 10
 
1.0%
1.02 13
1.3%
1.01 17
1.7%

Unnamed: 17
Real number (ℝ)

HIGH CORRELATION 

Distinct129
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8353875
Minimum0.4
Maximum1.375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-03T19:47:24.335329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.45
Q10.62
median0.825
Q31.0375
95-th percentile1.3
Maximum1.375
Range0.975
Interquartile range (IQR)0.4175

Descriptive statistics

Standard deviation0.25653756
Coefficient of variation (CV)0.30708809
Kurtosis-0.91224444
Mean0.8353875
Median Absolute Deviation (MAD)0.2125
Skewness0.21687798
Sum835.3875
Variance0.065811518
MonotonicityNot monotonic
2023-08-03T19:47:24.410037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 19
 
1.9%
0.55 17
 
1.7%
0.7 15
 
1.5%
0.85 14
 
1.4%
0.575 14
 
1.4%
1 14
 
1.4%
0.95 13
 
1.3%
0.6 13
 
1.3%
1.08 13
 
1.3%
1.1 13
 
1.3%
Other values (119) 855
85.5%
ValueCountFrequency (%)
0.4 8
0.8%
0.41 13
1.3%
0.42 9
0.9%
0.43 13
1.3%
0.44 2
 
0.2%
0.45 6
0.6%
0.46 8
0.8%
0.47 6
0.6%
0.48 11
1.1%
0.49 6
0.6%
ValueCountFrequency (%)
1.375 5
0.5%
1.3625 2
 
0.2%
1.35 12
1.2%
1.3375 6
0.6%
1.325 10
1.0%
1.3125 7
0.7%
1.3 11
1.1%
1.2875 8
0.8%
1.275 6
0.6%
1.2625 12
1.2%

Unnamed: 18
Real number (ℝ)

HIGH CORRELATION 

Distinct180
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.939265
Minimum0.4
Maximum1.71875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-03T19:47:24.482575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.5
Q10.6875
median0.9125
Q31.1625
95-th percentile1.46875
Maximum1.71875
Range1.31875
Interquartile range (IQR)0.475

Descriptive statistics

Standard deviation0.30540585
Coefficient of variation (CV)0.32515408
Kurtosis-0.61726909
Mean0.939265
Median Absolute Deviation (MAD)0.2375
Skewness0.36524118
Sum939.265
Variance0.09327273
MonotonicityNot monotonic
2023-08-03T19:47:24.558788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6875 24
 
2.4%
0.625 17
 
1.7%
1 16
 
1.6%
0.75 15
 
1.5%
0.5375 15
 
1.5%
0.7 15
 
1.5%
1.35 13
 
1.3%
1.1875 13
 
1.3%
1.3125 13
 
1.3%
1.375 13
 
1.3%
Other values (170) 846
84.6%
ValueCountFrequency (%)
0.4 5
0.5%
0.41 7
0.7%
0.42 4
0.4%
0.43 6
0.6%
0.44 1
 
0.1%
0.45 1
 
0.1%
0.46 6
0.6%
0.47 2
 
0.2%
0.48 6
0.6%
0.49 3
0.3%
ValueCountFrequency (%)
1.71875 1
 
0.1%
1.703125 2
 
0.2%
1.6875 5
0.5%
1.671875 3
0.3%
1.65625 5
0.5%
1.640625 3
0.3%
1.625 5
0.5%
1.609375 7
0.7%
1.59375 2
 
0.2%
1.578125 3
0.3%

Unnamed: 19
Real number (ℝ)

HIGH CORRELATION 

Distinct312
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86798397
Minimum0.34
Maximum1.71875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-03T19:47:24.633229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.4502875
Q10.63
median0.8396875
Q31.073125
95-th percentile1.3679687
Maximum1.71875
Range1.37875
Interquartile range (IQR)0.443125

Descriptive statistics

Standard deviation0.2923435
Coefficient of variation (CV)0.33680749
Kurtosis-0.36555561
Mean0.86798397
Median Absolute Deviation (MAD)0.2146875
Skewness0.45349201
Sum867.98397
Variance0.085464722
MonotonicityNot monotonic
2023-08-03T19:47:24.704809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.584375 14
 
1.4%
0.6375 12
 
1.2%
1 11
 
1.1%
0.6875 10
 
1.0%
0.85 10
 
1.0%
0.625 10
 
1.0%
0.595 10
 
1.0%
1.16875 9
 
0.9%
1.02 9
 
0.9%
0.945625 8
 
0.8%
Other values (302) 897
89.7%
ValueCountFrequency (%)
0.34 3
0.3%
0.3485 6
0.6%
0.357 2
 
0.2%
0.3655 5
0.5%
0.374 1
 
0.1%
0.3825 1
 
0.1%
0.391 5
0.5%
0.3995 2
 
0.2%
0.4 2
 
0.2%
0.408 4
0.4%
ValueCountFrequency (%)
1.71875 1
 
0.1%
1.6875 4
0.4%
1.671875 2
0.2%
1.65625 3
0.3%
1.640625 1
 
0.1%
1.625 2
0.2%
1.609375 3
0.3%
1.59375 2
0.2%
1.578125 2
0.2%
1.5625 1
 
0.1%

Unnamed: 20
Real number (ℝ)

HIGH CORRELATION 

Distinct324
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean498.819
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-03T19:47:24.781388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50
Q1250
median500
Q3750.25
95-th percentile948.15
Maximum1000
Range999
Interquartile range (IQR)500.25

Descriptive statistics

Standard deviation288.811
Coefficient of variation (CV)0.57898957
Kurtosis-1.2007498
Mean498.819
Median Absolute Deviation (MAD)250
Skewness0.0012458596
Sum498819
Variance83411.792
MonotonicityIncreasing
2023-08-03T19:47:24.855659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
760 13
 
1.3%
259 12
 
1.2%
455 9
 
0.9%
133 9
 
0.9%
386 9
 
0.9%
904 9
 
0.9%
700 8
 
0.8%
312 8
 
0.8%
820 8
 
0.8%
536 8
 
0.8%
Other values (314) 907
90.7%
ValueCountFrequency (%)
1 3
0.3%
4 2
0.2%
6 2
0.2%
8 2
0.2%
10 2
0.2%
12 1
 
0.1%
13 1
 
0.1%
14 2
0.2%
16 1
 
0.1%
17 2
0.2%
ValueCountFrequency (%)
1000 1
 
0.1%
997 3
0.3%
996 1
 
0.1%
994 2
 
0.2%
993 1
 
0.1%
988 5
0.5%
987 1
 
0.1%
985 2
 
0.2%
983 2
 
0.2%
979 4
0.4%

Rank
Real number (ℝ)

HIGH CORRELATION 

Distinct324
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean498.819
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-03T19:47:24.930498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50
Q1250
median500
Q3750.25
95-th percentile948.15
Maximum1000
Range999
Interquartile range (IQR)500.25

Descriptive statistics

Standard deviation288.811
Coefficient of variation (CV)0.57898957
Kurtosis-1.2007498
Mean498.819
Median Absolute Deviation (MAD)250
Skewness0.0012458596
Sum498819
Variance83411.792
MonotonicityIncreasing
2023-08-03T19:47:25.002362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
760 13
 
1.3%
259 12
 
1.2%
455 9
 
0.9%
133 9
 
0.9%
386 9
 
0.9%
904 9
 
0.9%
700 8
 
0.8%
312 8
 
0.8%
820 8
 
0.8%
536 8
 
0.8%
Other values (314) 907
90.7%
ValueCountFrequency (%)
1 3
0.3%
4 2
0.2%
6 2
0.2%
8 2
0.2%
10 2
0.2%
12 1
 
0.1%
13 1
 
0.1%
14 2
0.2%
16 1
 
0.1%
17 2
0.2%
ValueCountFrequency (%)
1000 1
 
0.1%
997 3
0.3%
996 1
 
0.1%
994 2
 
0.2%
993 1
 
0.1%
988 5
0.5%
987 1
 
0.1%
985 2
 
0.2%
983 2
 
0.2%
979 4
0.4%

Value
Real number (ℝ)

HIGH CORRELATION 

Distinct319
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88171409
Minimum0.34
Maximum1.71875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-08-03T19:47:25.073348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.45655625
Q10.64953125
median0.86
Q31.075
95-th percentile1.40625
Maximum1.71875
Range1.37875
Interquartile range (IQR)0.42546875

Descriptive statistics

Standard deviation0.29352451
Coefficient of variation (CV)0.33290214
Kurtosis-0.45247192
Mean0.88171409
Median Absolute Deviation (MAD)0.213125
Skewness0.42990252
Sum881.71409
Variance0.086156637
MonotonicityDecreasing
2023-08-03T19:47:25.142732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6375 13
 
1.3%
1.0625 12
 
1.2%
0.9 11
 
1.1%
0.8925 9
 
0.9%
1.2375 9
 
0.9%
0.5 9
 
0.9%
0.945625 9
 
0.9%
0.85 9
 
0.9%
0.825 8
 
0.8%
0.6875 8
 
0.8%
Other values (309) 903
90.3%
ValueCountFrequency (%)
0.34 1
 
0.1%
0.357 3
0.3%
0.374 1
 
0.1%
0.3825 2
 
0.2%
0.391 1
 
0.1%
0.3995 5
0.5%
0.4 1
 
0.1%
0.408 2
 
0.2%
0.41 2
 
0.2%
0.4165 4
0.4%
ValueCountFrequency (%)
1.71875 3
0.3%
1.703125 2
0.2%
1.671875 2
0.2%
1.65625 2
0.2%
1.640625 2
0.2%
1.625 1
 
0.1%
1.609375 1
 
0.1%
1.59375 2
0.2%
1.5625 1
 
0.1%
1.546875 2
0.2%

Interactions

2023-08-03T19:47:20.149047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:13.666851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:14.356009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:15.078915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:15.785226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:16.510846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:17.286029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:18.003968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:18.726804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:19.431856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:20.212317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:13.724433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:14.419407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:15.140468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:15.849403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:16.648697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:17.347062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:18.068755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:18.785873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:19.492980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:20.276852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:13.786394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:14.483898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:15.202886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:15.917490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:16.712780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:17.413675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:18.135853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:18.851069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:19.557839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:20.342319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:13.850318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:14.548948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:15.266929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:15.982915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:16.775160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:17.480867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:18.199455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:18.915600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:19.619617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:20.408864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:13.914831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:14.618994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:15.333858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:16.052375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:16.843207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:17.547879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:18.272123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:18.984731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:19.686352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:20.471033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:13.977584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:14.685073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:15.398061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:16.119112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:16.903773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:17.610356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:18.335812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:19.048811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:19.754755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:20.616912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:14.042048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:14.751324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:15.463278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:16.185427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:16.969816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:17.680966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:18.402452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:19.115855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:19.822000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:20.682451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:14.106968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:14.821133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:15.529343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:16.254429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:17.034868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:17.747821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:18.469748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:19.182360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:19.889453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:20.745100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:14.169233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:14.884846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:15.590848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:16.316360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:17.095819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:17.809830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:18.532456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:19.242054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:19.949863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:20.807830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:14.229842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:14.945924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:15.654055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:16.382272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:17.157343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:17.874925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:18.595920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:19.303925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-03T19:47:20.012723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-03T19:47:25.212848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
past_3_years_bike_related_purchasestenurepostcodeproperty_valuationUnnamed: 16Unnamed: 17Unnamed: 18Unnamed: 19Unnamed: 20RankValuegenderjob_industry_categorywealth_segmentowns_carstate
past_3_years_bike_related_purchases1.000-0.0350.012-0.0160.0430.0500.1320.131-0.006-0.0060.0060.0000.0310.0000.0000.054
tenure-0.0351.0000.023-0.0100.0140.0150.0110.0210.0040.004-0.0040.1100.0000.0000.0790.000
postcode0.0120.0231.000-0.5580.0100.034-0.092-0.092-0.041-0.0410.0410.0840.0000.0470.0960.973
property_valuation-0.016-0.010-0.5581.000-0.041-0.0670.1620.1510.0110.011-0.0110.0000.0000.0310.0740.294
Unnamed: 160.0430.0140.010-0.0411.0000.9340.8920.872-0.015-0.0150.0150.0000.0530.0370.0000.055
Unnamed: 170.0500.0150.034-0.0670.9341.0000.9430.920-0.006-0.0060.0060.0000.0590.0000.4710.008
Unnamed: 180.1320.011-0.0920.1620.8920.9431.0000.972-0.008-0.0080.0080.0000.0000.0000.3380.000
Unnamed: 190.1310.021-0.0920.1510.8720.9200.9721.000-0.005-0.0050.0050.0000.0070.2080.3170.056
Unnamed: 20-0.0060.004-0.0410.011-0.015-0.006-0.008-0.0051.0001.000-1.0000.0000.0270.0170.0000.000
Rank-0.0060.004-0.0410.011-0.015-0.006-0.008-0.0051.0001.000-1.0000.0000.0270.0170.0000.000
Value0.006-0.0040.041-0.0110.0150.0060.0080.005-1.000-1.0001.0000.0000.0000.0000.0490.013
gender0.0000.1100.0840.0000.0000.0000.0000.0000.0000.0000.0001.0000.3410.0140.0000.031
job_industry_category0.0310.0000.0000.0000.0530.0590.0000.0070.0270.0270.0000.3411.0000.0870.0000.054
wealth_segment0.0000.0000.0470.0310.0370.0000.0000.2080.0170.0170.0000.0140.0871.0000.0160.000
owns_car0.0000.0790.0960.0740.0000.4710.3380.3170.0000.0000.0490.0000.0000.0161.0000.053
state0.0540.0000.9730.2940.0550.0080.0000.0560.0000.0000.0130.0310.0540.0000.0531.000

Missing values

2023-08-03T19:47:20.914499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-03T19:47:21.087644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-03T19:47:21.197868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

first_namelast_namegenderpast_3_years_bike_related_purchasesDOBjob_titlejob_industry_categorywealth_segmentdeceased_indicatorowns_cartenureaddresspostcodestatecountryproperty_valuationUnnamed: 16Unnamed: 17Unnamed: 18Unnamed: 19Unnamed: 20RankValue
0ChickieBristerMale861957-07-12General ManagerManufacturingMass CustomerNYes1445 Shopko Center4500QLDAustralia6.00.680.85001.062500.903125111.718750
1MorlyGeneryMale691970-03-22Structural EngineerPropertyMass CustomerNNo1614 Mccormick Park2113NSWAustralia11.00.610.61000.762500.648125111.718750
2ArdelisForresterFemale101974-08-28Senior Cost AccountantFinancial ServicesAffluent CustomerNNo105 Colorado Crossing3505VICAustralia5.00.930.93000.930000.930000111.718750
3LucineStuttFemale641979-01-28Account Representative IIIManufacturingAffluent CustomerNYes5207 Annamark Plaza4814QLDAustralia1.00.460.57500.575000.575000441.703125
4MelindaHadleeFemale341965-09-21Financial AnalystFinancial ServicesAffluent CustomerNNo19115 Montana Place2093NSWAustralia9.00.620.62000.775000.775000441.703125
5DruciBrandliFemale391951-04-29Assistant Media PlannerEntertainmentHigh Net WorthNYes2289105 Pearson Terrace4075QLDAustralia7.00.911.13751.137501.137500661.671875
6RutledgeHalltMale231976-10-06Compensation AnalystFinancial ServicesMass CustomerNNo87 Nevada Crossing2620NSWAustralia7.00.880.88000.880000.748000661.671875
7NancieVianFemale741972-12-27Human Resources Assistant IIRetailMass CustomerNYes1085 Carioca Point4814QLDAustralia5.00.971.21251.212501.030625881.656250
8DuffKarlowiczMale501972-04-28Speech PathologistManufacturingMass CustomerNYes5717 West Drive2200NSWAustralia10.00.460.57500.718750.610938881.656250
9BarthelDocketMale721985-08-02Accounting Assistant IVITMass CustomerNYes1780 Scofield Junction4151QLDAustralia5.00.710.88750.887500.75437510101.640625
first_namelast_namegenderpast_3_years_bike_related_purchasesDOBjob_titlejob_industry_categorywealth_segmentdeceased_indicatorowns_cartenureaddresspostcodestatecountryproperty_valuationUnnamed: 16Unnamed: 17Unnamed: 18Unnamed: 19Unnamed: 20RankValue
990JermaineBagshaweFemale601954-05-14Help Desk OperatorPropertyMass CustomerNYes9260 Briar Crest Drive4209QLDAustralia6.00.951.18751.1875001.0093759889880.3995
991BryanJachtymMale591974-05-15Automation Specialist IManufacturingMass CustomerNYes1556 Moland Crossing3356VICAustralia3.00.420.52500.5250000.4462509889880.3995
992RenieLaundonFemale321973-12-18Assistant Media PlannerEntertainmentMass CustomerNYes81 Shelley Pass4118QLDAustralia3.00.740.92500.9250000.7862509939930.3910
993WeidarEtheridgeMale381959-07-13Compensation AnalystFinancial ServicesMass CustomerNYes60535 Jay Point2422NSWAustralia4.00.420.52500.5250000.4462509949940.3825
994DathaFishburnFemale151990-07-02Office Assistant IVRetailMass CustomerNNo36 Caliangt Way3079VICAustralia12.00.990.99001.2375001.0518759949940.3825
995FerdinandRomanettiMale601959-10-07ParalegalFinancial ServicesAffluent CustomerNNo92 Sloan Way2200NSWAustralia7.00.910.91000.9100000.9100009969960.3740
996BurkWortleyMale222001-10-17Senior Sales AssociateHealthMass CustomerNNo604 Union Crossing2196NSWAustralia10.01.051.05001.3125001.1156259979970.3570
997MelloneyTembyFemale171954-10-05Budget/Accounting Analyst IVFinancial ServicesAffluent CustomerNYes1533475 Fair Oaks Junction4702QLDAustralia2.01.041.30001.3000001.3000009979970.3570
998DickieCubbiniMale301952-12-17Financial AdvisorFinancial ServicesMass CustomerNYes1957666 Victoria Way4215QLDAustralia2.00.750.93750.9375000.7968759979970.3570
999SylasDuffillMale561955-10-02Staff Accountant IVPropertyMass CustomerNYes1421875 Grover Drive2010NSWAustralia9.01.051.31251.6406251.394531100010000.3400